Understanding Long Videos with Multimodal Language Models

• URL: http://arxiv.org/abs/2403.16998v2
• Date: Mon, 11 Nov 2024 17:56:29 GMT
• Title: Understanding Long Videos with Multimodal Language Models
• Authors: Kanchana Ranasinghe, Xiang Li, Kumara Kahatapitiya, Michael S. Ryoo,
• Abstract summary: Large Language Models (LLMs) have allowed recent approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world knowledge and strong reasoning skills of underlying LLMs influence this strong performance. Our resulting Multimodal Video Understanding framework demonstrates state-of-the-art performance across multiple video understanding benchmarks.
• Score: 44.78900245769057
• License:
• Abstract: Large Language Models (LLMs) have allowed recent LLM-based approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world knowledge and strong reasoning skills of underlying LLMs influence this strong performance. Surprisingly, we discover that LLM-based approaches can yield surprisingly good accuracy on long-video tasks with limited video information, sometimes even with no video specific information. Building on this, we exploring injecting video-specific information into an LLM-based framework. We utilize off-the-shelf vision tools to extract three object-centric information modalities from videos and then leverage natural language as a medium for fusing this information. Our resulting Multimodal Video Understanding (MVU) framework demonstrates state-of-the-art performance across multiple video understanding benchmarks. Strong performance also on robotics domain tasks establish its strong generality. Our code will be released publicly.

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